Evergreen.AI is building enterprise-grade AI solutions and frameworks that transform how organizations operate. As an ML Researcher, you will focus on advancing machine learning methodologies, developing novel algorithms, and applying cutting-edge techniques to solve complex enterprise problems. This role emphasizes innovation and applied research while ensuring practical implementation in production environments.
Key Responsibilities:
Research & Development
- Explore and develop new ML algorithms for NLP, computer vision, and multimodal applications.
- Conduct experiments with state-of-the-art architectures (Transformers, Diffusion Models, Graph Neural Networks).
- Publish findings internally and contribute to reusable frameworks for Evergreen.AI offerings.
Model Prototyping & Evaluation
- Build prototypes for LLMs, deep learning models, and hybrid architectures.
- Design evaluation frameworks for performance, robustness, and fairness using OpenAI Evals, DeepEval, or custom metrics.
Collaboration & Integration
- Work closely with AI engineers to transition research models into production-ready systems.
- Support agentic workflows and orchestration frameworks (e.g., LangChain, CrewAI) with research-driven enhancements.
Data & Knowledge Management
- Experiment with retrieval-augmented generation (RAG), knowledge graphs, and vector databases for improved model performance.
- Ensure research aligns with enterprise data governance and compliance standards.
Continuous Innovation
- Stay updated with the latest advancements in ML/AI and apply them to Evergreen.AI's product roadmap.
- Contribute to internal knowledge-sharing sessions and technical documentation.
Qualifications:
- Ph.D. or Master's degree in Computer Science, Machine Learning, or related field.
- 4+ years of experience in ML research with a strong publication record or applied research experience.
- Proficiency in Python, PyTorch, TensorFlow, and ML libraries (Hugging Face, JAX).
- Familiarity with LLMs, agentic workflows, and knowledge graph-based systems.
- Strong understanding of optimization techniques, evaluation metrics, and experimental design.
- Excellent problem-solving, analytical, and communication skills.
